This Perspective highlights the importance of preparing the energy materials research workforce for opportunities in automation and artificial intelligence and describes a successful summer school on the topic.

This Perspective describes various approaches to carbon dioxide removal and their societal impacts, highlighting the promise of reactive carbon capture methods.

This study reports the electric-field control of solid-state heat flow in a relaxor-based ferroelectric material, with the implications for robust thermal switching devices.

r${}^2$SCAN provides a reliable, computationally efficient, and parameter-free route to predict electron–phonon coupling across diverse materials. This accuracy across materials classes can enable predictive, high-throughput screening for superconducting, thermoelectric, and other energy-relevant applications.

A comprehensive kinetic model of charge and excited state dynamics at the donor-acceptor interface enhances the physical understanding of advanced organic solar cells and provides direction for improvements in the next generation of devices.

A quantum machine learning method uses fewer than ten qubits to perform resource-efficient, scalable simulations of materials properties, with implications for quantum-assisted design of energy materials.

An optimized zero-vacuum-gap thermophotonic device promises improved waste-heat recovery, with substantial gains in efficiency and power for low-to-moderate temperature sources.

Advanced computational modeling provides deep insight into the properties of Nb${}_3$Sn, a high-performance superconductor used in fusion reactors, particle accelerators, and emerging technologies, and reveals practical strategies for improving the material’s performance.

A hybrid approach for large building electrification highlights that capturing, storing, and reusing waste heat enhances the feasibility of a system with solar power, lithium-ion batteries, Power-to-Heat-to-Power Storage systems, and electric heat pumps.

A high-throughput computational study explores the thermoelectric properties of high-entropy skutterudite compounds and predicts the synthetic accessibility of these materials.

A theory incorporates electron-ion correlations into its description of the electric double layer at the interface between a metal and an ionic solution. With the inclusion of these correlations, the theory demonstrates agreement with experimental capacitance measurements.

To support safe and efficient storage of cryogenic hydrogen, this work demonstrates an in-situ method to precisely monitor the exothermic ortho-para conversion in real time.

An autonomous experimental method utilizes combinatorial synthesis, accelerated electrochemical impedance spectroscopy, and active learning to optimize thin-film solid oxide electrodes.

An advanced framework simultaneously models elastic constants and bending rigidities of materials, showing good agreement with experimental precedent. This improved modeling of mechanical properties has implications for the development of new materials for solid-state batteries and other energy technologies.